Selection of corners and/or margins using statistical static timing analysis of an integrated circuit

ABSTRACT

Examples of techniques for statistical static timing analysis of an integrated circuit are disclosed. In one example according to aspects of the present disclosure, a computer-implemented method is provided. The method comprises performing an initial statistical static timing analysis of the integrated circuit to create a parameterized model of the integrated circuit for a plurality of paths using a plurality of timing corners to calculate a timing value for each of the plurality of paths, each of the plurality of timing corners representing a set of timing performance parameters. The method further comprises determining at least one worst timing corner from the parameterized model for each of the plurality of paths based on the initial statistical static timing analysis and calculated timing value for each of the plurality of paths. The method also comprises performing a subsequent analysis of the integrated circuit using the at least one worst timing corner.

BACKGROUND

The present disclosure relates to techniques for integrated circuitdesign and fabrication and, more particularly, to techniques forperforming statistical static timing analysis of an integrated circuit.

One form of performance analysis used during integrated circuit (IC)design is static timing analysis (STA). STA is an important process bywhich one identifies any circuit races/hazards which could cause a chipto malfunction, verifies the operational speed of a chip, and identifiesthe paths which limit the operational speed. STA typically operates on atiming graph, in which nodes represent electrical nodes (e.g., circuitpins) at which signals may make transitions at various times, and edges,or segments, representing the delays of the circuits and/or wiresconnecting the nodes. Although it may report performance-limiting paths,typical STA methods do not actually operate on paths (of which there maybe an exponentially large number), and instead use a “block-based”approach to compute and propagate forward signal arrival timesreflecting the earliest and/or latest possible times that signaltransitions can occur at nodes in the timing graph. As a result, STA isefficient, allowing for rapid estimation of IC timing on very largedesigns as compared to other approaches (e.g., transient simulation).

SUMMARY

In accordance with aspects of the present disclosure, acomputer-implemented method for a statistical static timing analysis ofan integrated circuit is provided. The method comprises performing, by aprocessor, an initial statistical static timing analysis of theintegrated circuit to create a parameterized model of the integratedcircuit for a plurality of paths using a plurality of timing corners tocalculate a timing value for each of the plurality of paths, each of theplurality of timing corners representing a set of timing performanceparameters. The method further comprises determining, by the processor,at least one worst timing corner from the parameterized model for eachof the plurality of paths based on the initial statistical static timinganalysis and calculated timing value for each of the plurality of paths.The method also comprises performing, by the processor, a subsequentanalysis of the integrated circuit using the at least one worst timingcorner.

In accordance with additional aspects of the present disclosure, asystem for statistical static timing analysis of an integrated circuitis disclosed. The system comprises a processor in communication with oneor more types of memory. The processor is configured to perform aninitial statistical static timing analysis of the integrated circuit tocreate a parameterized model of the integrated circuit for a pluralityof paths using a plurality of timing corners to calculate a timing valuefor each of the plurality of paths, each of the plurality of timingcorners representing a set of timing performance parameters. Theprocessor is further configured to determine at least one worst timingcorner from the parameterized model for each of the plurality of pathsbased on the initial statistical static timing analysis and calculatedtiming value for each of the plurality of paths. The processor is alsoconfigured to perform a subsequent analysis of the integrated circuitusing the at least one worst timing corner.

In accordance with yet additional aspects of the present disclosure, acomputer program product for statistical static timing analysis of anintegrated circuit is provided. The computer program product comprises anon-transitory storage medium readable by a processing circuit andstoring instructions for execution by the processing circuit forperforming a method. The method comprises performing an initialstatistical static timing analysis of the integrated circuit to create aparameterized model of the integrated circuit for a plurality of pathsusing a plurality of timing corners to calculate a timing value for eachof the plurality of paths, each of the plurality of timing cornersrepresenting a set of timing performance parameters. The method furthercomprises determining at least one worst timing corner from theparameterized model for each of the plurality of paths based on theinitial statistical static timing analysis and calculated timing valuefor each of the plurality of paths. The method also comprises performinga subsequent analysis of the integrated circuit using the at least oneworst timing corner.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter which is regarded as the invention is particularlypointed out and distinctly claimed in the claims at the conclusion ofthe specification. The forgoing and other features, and advantagesthereof, are apparent from the following detailed description taken inconjunction with the accompanying drawings in which:

FIG. 1 illustrates a block diagram of a processing system forimplementing the techniques described herein according to aspects of thepresent disclosure;

FIG. 2 illustrates a block diagram of a processing system forstatistical static timing analysis of an integrated circuit according toaspects of the present disclosure;

FIG. 3 illustrates a flow diagram of a method for statistical statictiming analysis of an integrated circuit according to aspects of thepresent disclosure;

FIG. 4 illustrates a flow diagram of a method for statistical statictiming analysis of an integrated circuit according to aspects of thepresent disclosure; and

FIG. 5 illustrates a flow diagram of a method for statistical statictiming analysis of an integrated circuit according to aspects of thepresent disclosure.

DETAILED DESCRIPTION

In accordance with aspects of the present disclosure, techniquesincluding systems, methods, and/or computer program products forstatistical static timing analysis of an integrated circuit areprovided. The selection of deterministic timing corners formulti-corner, multi-mode (MCMM) static timing analysis and/oroptimization are currently performed by inspection, heuristics, and/or“rule of thumb” techniques. However, these techniques include inherentrisks as actual hardware response may not be accurately modeled.

It is known that statistical static timing analysis (SSTA) may improvecoverage and lower the risk of not accurately modeling the circuithardware. However, SSTA is expensive in terms of turn-around time andprocessing system memory use. For example, SSTA may take days to model aparticular IC design. Therefore, IC designers may still usedeterministic timing for non-signoff STA and/or optimization even whenSSTA is used for signoff of the final IC design. However, this approachmay introduce mis-correlation between the timing signoff and non-signofftiming and/or optimization environments. Some IC designers apply MCMMfor timing signoff, but this approach retains the inherent risks of notaccurately modeling the actual hardware.

For advanced IC designs, the proliferation of sources of variation, andthe inter-dependencies among them, is increasing. For example, thefollowing may complicate existing approaches to IC design modeling: thedifficulty of effective timing corner selection using existing methods;the number of timing corners required to cover the design space; thecriticality of balancing the number of timing corners analyzed andassociated margins; and the likelihood of additional design flowiterations, as well as hardware escapes.

In some implementations, the present techniques increase efficiency ofoptimal timing corner selection using SSTA, enabling reduced turn-aroundtime and memory use of the processing system performing deterministicnon-signoff STA and/or non-SSTA-based optimization. In addition, thepresent techniques provide more accurate and reliable modeling of ICdesigns by providing accurate and efficient identification of criticaltiming corners. The present techniques also improve correlation betweendeterministic non-signoff STA and/or non-SSTA-based optimization andSSTA signoff. Additionally, the present techniques enable iterativeupdates to timing corner selection as the IC design progresses. Theseand other advantages will be apparent from the description that follows.

FIG. 1 illustrates a block diagram of a processing system 100 forimplementing the techniques described herein according to aspects of thepresent disclosure. In examples, the processing system 100 has one ormore central processing units (processors) 101 a, 101 b, 101 c, etc.(collectively or generically referred to as processor(s) 101). Inaspects of the present disclosure, each processor 101 may include areduced instruction set computer (RISC) microprocessor. Processors 101are coupled to system memory (e.g., random access memory (RAM)) 114 andvarious other components via a system bus 113. Read only memory (ROM)102 is coupled to the system bus 113 and may include a basicinput/output system (BIOS), which controls certain basic functions ofthe processing system 100.

FIG. 1 further illustrates an input/output (I/O) adapter 107 and acommunications adapter 106 coupled to the system bus 113. I/O adapter107 may be a small computer system interface (SCSI) adapter thatcommunicates with a hard disk 103 and/or tape storage drive 105 or anyother similar component. I/O adapter 107, hard disk 103, and tapestorage device 105 are collectively referred to herein as mass storage104. Operating system 120 for execution on the processing system 100 maybe stored in mass storage 104. A network adapter 106 interconnects bus113 with an outside network 116 enabling the processing system 100 tocommunicate with other such systems.

A screen (e.g., a display monitor) 115 is connected to system bus 113 bydisplay adaptor 112, which may include a graphics adapter to improve theperformance of graphics intensive applications and a video controller.In one aspect of the present disclosure, adapters 106, 107, and 112 maybe connected to one or more I/O busses that are connected to system bus113 via an intermediate bus bridge (not shown). Suitable I/O buses forconnecting peripheral devices such as hard disk controllers, networkadapters, and graphics adapters typically include common protocols, suchas the Peripheral Component Interconnect (PCI). Additional input/outputdevices are shown as connected to system bus 113 via user interfaceadapter 108 and display adapter 112. A keyboard 109, mouse 110, andspeaker 111 all interconnected to bus 113 via user interface adapter108, which may include, for example, a Super I/O chip integratingmultiple device adapters into a single integrated circuit.

In some aspects of the present disclosure, the processing system 100includes a graphics processing unit 130. Graphics processing unit 130 isa specialized electronic circuit designed to manipulate and alter memoryto accelerate the creation of images in a frame buffer intended foroutput to a display. In general, graphics processing unit 130 is veryefficient at manipulating computer graphics and image processing, andhas a highly parallel structure that makes it more effective thangeneral-purpose CPUs for algorithms where processing of large blocks ofdata is done in parallel.

Thus, as configured in FIG. 1, the processing system 100 includesprocessing capability in the form of processors 101, storage capabilityincluding system memory 114 and mass storage 104, input means such askeyboard 109 and mouse 110, and output capability including speaker 111and display 115. In some aspects of the present disclosure, a portion ofsystem memory 114 and mass storage 104 collectively store an operatingsystem such as the AIX® operating system from IBM Corporation tocoordinate the functions of the various components shown in FIG. 1.

FIG. 2 illustrates a block diagram of a processing system 200 forstatistical static timing analysis of an integrated circuit according toexamples of the present disclosure. The various components, modules,engines, etc. described regarding FIG. 2 may be implemented asinstructions stored on a computer-readable storage medium, as hardwaremodules, as special-purpose hardware (e.g., application specifichardware, application specific integrated circuits (ASICs), as embeddedcontrollers, hardwired circuitry, etc.), or as some combination orcombinations of these. In examples, the engine(s) described herein maybe a combination of hardware and programming. The programming may beprocessor executable instructions stored on a tangible memory, and thehardware may include processors 101 for executing those instructions.Thus system memory 114 of FIG. 1 can be said to store programinstructions that when executed by the processor 201 implements theengines described herein. Other engines may also be utilized to includeother features and functionality described in other examples herein.

In the example of FIG. 2, the processing system 200 comprises aprocessor 201, a statistical static timing analysis (SSTA) engine 202, aworst timing corner determining engine 204, and a user input engine 206.Alternatively or additionally, the processing system 200 may includededicated hardware, such as one or more integrated circuits, ApplicationSpecific Integrated Circuits (ASICs), Application Specific SpecialProcessors (ASSPs), Field Programmable Gate Arrays (FPGAs), or anycombination of the foregoing examples of dedicated hardware, forperforming the techniques described herein.

SSTA engine 202 performs a statistical static timing analysis of anintegrated circuit (i.e., an integrated circuit design) to create aparameterized model of the integrated circuit for a plurality of pathson the IC using a plurality of timing corners. The timing cornersrepresent a set of timing performance parameters, which, for example,may include temperature, voltage, process, and the like. A timing slackis calculated for each of the paths. In this way, SSTA engine 202identifies a set of timing performance parameter values (i.e., a “timingcorner”), which leads to a “worst” timing slack for each of the paths ina design of an integrated circuit by projecting a parameterized modelfor the timing slack to its lowest value. Although the presentdisclosure references timing slack calculations, other examples oftiming quantities may be applied, which include gate or wire delay, pathdelay, arrival time, required arrival time, slew, guard time, assertion,and/or adjust. Examples of parameterized models include a canonicalslack model, a canonical slew model, a canonical arrival time model,etc. The “worst” slack, as used herein, may refer to the lowest valuefor the slack (i.e., the most negative value for slack).

SSTA engine 202 indicates which timing corner(s) of a plurality oftiming corners could be used by deterministic timing tools for optimalcoverage and/or by optimization tools in efficient, deterministic,iterative operations to provide a desired correlation to SSTA-basedsignoff timing of the IC design. For example, for a given number oftiming corners, SSTA engine 202 defines the minimum margin needed tomeet a desired coverage to include the timing corners. This aspectcorresponds to the method 300 illustrated in FIG. 3 and discussed below.

SSTA engine 202 may also indicate a margin to be applied such thattiming corner ‘n’ also covers timing corners ‘n+1, n+2, . . . ’ byanalyzing slack differences between and among the timing corners. Inthis way, multiple timing corners may be captured within the margin. Forexample, for a given margin, SSTA engine 202 identifies the timingcorners needed to meet a desired coverage. This aspect corresponds tothe method 400 illustrated in FIG. 4 and discussed below.

Worst timing corner determining engine 204 determines a worst timingcorner from the plurality of timing corners of the parameterized modelfor each of the plurality of paths. Worst timing corner determiningengine 204 may utilize the initial statistical static timing analysisand the calculated timing slack for each of the paths. A timing cornermay represent a set of timing performance parameters such astemperature, voltage, process, and the like. The “worst” timing corner,as used herein, may refer to the timing corner with a set of parametervalues returning the worst or lowest value slack. Worst cornerdetermining engine 204 uses the parameterized model to project the worstcorner(s) (i.e., the set of timing performance parameters that providethe worst or lowest value slack as projected by the parameterizedmodel).

After the worst timing corner (or, in examples, timing corners) isselected, the worst timing corner(s) may be passed to anoptimization/deterministic timing engine 208 to perform optimizationand/or deterministic timing operations to optimize the IC setup usingthe worst timing corner(s). This enables optimization/deterministictiming engine 208 to focus on the worst corner(s), where the most timingfixup work is needed. In additional examples, performing the subsequentdeterministic STA and/or optimization operation may also includeapplying a margin to the at least one worst timing corner and performingthe subsequent operation using the margin. This enables the subsequentoperation to cover variation around the worst corner. This may bereferred to as wrapping a corner with a margin, which also can enablemultiple corners to be captured at once. The margin is applied as achange in delay in examples.

In examples, the margin includes not just modeling the base parametersbut also modeling variation in process and contingencies for slightvariation. For example, the variation in process may include random andsystematic on chip variation. The contingencies for slight variation mayinclude voltage droops, for example.

User input engine 206 may receive a user inputs, such as via a keyboard,mouse, touch screen, or other input device. The user inputs may indicatewhat type of analysis to perform on which paths (e.g., clock versusdata, test type, area/region, early/late, etc.). In addition, other usercriteria may be received via user input engine 206 which may be utilizedto filter slacks based on user criteria (e.g., positive vs. negative,etc.).

FIG. 3 illustrates a flow diagram of a method 300 for statistical statictiming analysis of an integrated circuit according to aspects of thepresent disclosure. The method 300 begins at block 302 and continues toblock 304.

At block 304, the method 300 includes performing, such as by aprocessor, an initial statistical static timing analysis of theintegrated circuit to create a parameterized model of the integratedcircuit for a plurality of paths using a plurality of timing corners tocalculate a timing slack for each of the plurality of paths. Theplurality of timing corners represents a set of timing performanceparameters (e.g., temperature, voltage, process, etc.). Theparameterized model may be a canonical slack model, a canonical slewmodel, a canonical arrival time model, etc.

At block 306, the method 300 includes determining, by the processor, atleast one worst timing corner from the parameterized model for each ofthe plurality of paths based on the initial statistical static timinganalysis and calculated timing value (e.g., slack, arrival time, slew,etc.) for each of the plurality of paths.

At block 308, the method 300 includes selecting a number of timingcorners for further optimization/deterministic timing testing aspre-determined by the efficiency of optimization and/or timing tools.

At block 310, the method 300 includes delivering a timing setup (e.g.,the selected timing corners) to the optimization and/or deterministictiming tools. In examples, this may include performing a subsequentdeterministic static timing analysis using the selected at least oneworst timing corner. The method 300 continues to block 312 andterminates.

Additional processes also may be included, and it should be understoodthat the processes depicted in FIG. 3 represent illustrations, and thatother processes may be added or existing processes may be removed,modified, or rearranged without departing from the scope and spirit ofthe present disclosure.

FIG. 4 illustrates a flow diagram of a method 400 for statistical statictiming analysis of an integrated circuit according to aspects of thepresent disclosure according to examples of the present disclosure. Themethod 400 begins at block 402 and continues to block 404.

At block 404, the method 400 includes performing, such as by aprocessor, an initial statistical static timing analysis of theintegrated circuit to create a parameterized model of the integratedcircuit for a plurality of paths using a plurality of timing corners tocalculate a timing slack for each of the plurality of paths. Theplurality of timing corners represents a set of timing performanceparameters (e.g., temperature, voltage, process, etc.). Theparameterized model may be a canonical slack model, a canonical slewmodel, a canonical arrival time model, etc.

At block 406, the method 400 includes determining, by the processor, atleast one worst timing corner from the parameterized model for each ofthe plurality of paths based on the initial statistical static timinganalysis and calculated timing value (e.g., slack, arrival time, slew,etc.) for each of the plurality of paths.

At block 408, the method 400 includes selecting margins to provide adesired coverage or quality of results for a number of selected timingcorners. For example, if a group timing corners is selected, margins maybe selected to make them more effective (i.e., cover more of theapplication space, or cover other timing corners).

At block 410, the method 400 includes delivering a timing setup (e.g.,the selected timing corners and margins) to the optimization and/ordeterministic timing tools. In examples, this may include performing asubsequent statistical static timing analysis using the selected atleast one worst timing corner. The method 400 continues to block 412 andterminates.

Additional processes also may be included, and it should be understoodthat the processes depicted in FIG. 4 represent illustrations, and thatother processes may be added or existing processes may be removed,modified, or rearranged without departing from the scope and spirit ofthe present disclosure.

FIG. 5 illustrates a flow diagram of a method 500 for statistical statictiming analysis of an integrated circuit according to aspects of thepresent disclosure. The method 500 begins at block 502 and continues toblock 504.

At block 504, a user may specify criteria for paths and/or tests. Forexample, a user may specify particular paths to be tested, certain teststo be performed, certain regions of the design to be analyzed, etc.

At block 506, statistical static timing analysis is performed for eachof the paths of the IC to create slack canonicals.

At block 508, the slack canonicals for each of the selected paths of theIC are projected to the worst value.

At block 510, user criteria may be applied to filter the slackcanonicals. This enables a user to define the “worst value” based on adesired test, outcome, or which parameters are of more or lessimportance, for example. The user may also specify certain paths to beexcluded, such as paths that return a positive slack.

At block 512, the worst timing corners are determined, for example,using pre-determined test metrics. Examples of predetermined testmetrics may be whether a setup test is to be applied or whether a holdtest is to be applied. In examples, a setup test enables determiningwhether a delay is too long while a hold tests enables determiningwhether data is captured in the correct clock cycle. Which of the timingcorners are considered “worst” may vary depending on which type of testis being applied. The determination of the worst corners(s) may be doneby techniques familiar to a person having ordinary skill in the art.

After the worst timing corners are determined at block 512, one of twooptions may be implemented. It should be understood that one of block514 and block 516 is implemented at a time. In one example, at block514, a number of timing corners are selected as determined by theefficiency of the optimization/deterministic timing tool. For example,four timing corners may be selected for an optimization/deterministictiming tool with a four corner maximum. However, other numbers ofcorners may be selected depending on the efficiency of theoptimization/deterministic timing tool.

In another example, at block 516 a margin is selected to provide adesired coverage for a number of selected timing corners. For example,if four timing corners are selected, the margins to make them effectiveare selected. In examples, a desired quality of result (e.g., a measureof how close to a particular answer is desired) for the number ofselected corners may be implemented.

At block 518, the corner and/or margin timing setup information fromblocks 514 and 516 respectively is delivered to an optimization/timingtool. The method then proceeds to block 520 and ends. However, in otherexamples, the method 500 may be iterative, as illustrated by arrow 522to enable iteration as the IC design matures.

Although FIG. 5 is discussed with reference to “slack” timing, it shouldbe appreciated that other timing values of interest, such as arrivaltime, slew, etc. may be implemented instead and/or in addition to slack.Additional processes also may be included, and it should be understoodthat the processes depicted in FIG. 5 represent illustrations, and thatother processes may be added or existing processes may be removed,modified, or rearranged without departing from the scope and spirit ofthe present disclosure.

The present techniques may be implemented as a system, a method, and/ora computer program product. The computer program product may include acomputer readable storage medium (or media) having computer readableprogram instructions thereon for causing a processor to carry outaspects of the present disclosure.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present disclosure may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some examples, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present disclosure.

Aspects of the present disclosure are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to aspects of thepresent disclosure. It will be understood that each block of theflowchart illustrations and/or block diagrams, and combinations ofblocks in the flowchart illustrations and/or block diagrams, can beimplemented by computer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousaspects of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

What is claimed is:
 1. A computer-implemented method for statisticalstatic timing analysis of an integrated circuit, the method comprising:performing, by a processor, an initial statistical static timinganalysis of the integrated circuit to create a parameterized model ofthe integrated circuit for a plurality of paths using a plurality oftiming corners to calculate a timing value for each of the plurality ofpaths, each of the plurality of timing corners representing a set oftiming performance parameters; determining, by the processor, at leastone worst timing corner from the parameterized model for each of theplurality of paths based on the initial statistical static timinganalysis and calculated timing value for each of the plurality of paths;calculating a margin for each of the at least one worst timing corners;and performing, by the processor, a subsequent analysis of theintegrated circuit using the at least one worst timing corner, whereinperforming the subsequent analysis further comprises applying the marginto the at least one worst timing corner and performing the subsequentanalysis using the margin, wherein the subsequent analysis is one of anoptimization analysis and a deterministic timing analysis.
 2. Thecomputer-implemented method of claim 1, further comprising selecting theplurality of paths based on a first user criteria received from a user.3. The computer-implemented method of claim 2, further comprisingfiltering the timing values for each of the plurality of paths based ona second user criteria received from the user.
 4. Thecomputer-implemented method of claim 1, wherein the parameterized modelis a canonical slack model and the timing value is a slack.
 5. Thecomputer-implemented method of claim 1, wherein the parameterized modelis a canonical slew model and the timing value is a slew.
 6. Thecomputer-implemented method of claim 1, wherein the parameterized modelis a canonical arrival time model and the timing value is an arrivaltime.
 7. The computer-implemented method of claim 1, wherein the set oftiming performance parameters comprises a temperature value, a voltagevalue, and a process value.
 8. A system for statistical static timinganalysis of an integrated circuit, the system comprising: a processor incommunication with one or more types of memory, the processor configuredto: perform an initial statistical static timing analysis of theintegrated circuit to create a parameterized model of the integratedcircuit for a plurality of paths using a plurality of timing corners tocalculate a timing value for each of the plurality of paths, each of theplurality of timing corners representing a set of timing performanceparameters, determine at least one worst timing corner from theparameterized model for each of the plurality of paths based on theinitial statistical static timing analysis and calculated timing valuefor each of the plurality of paths, calculate a margin for each of theat least one worst timing corners; and perform a subsequent analysis ofthe integrated circuit using the at least one worst timing corner,wherein performing the subsequent analysis further comprises applyingthe margin to the at least one worst timing corner and performing thesubsequent analysis using the margin, wherein the subsequent analysis isone of an optimization analysis and a deterministic timing analysis. 9.The system of claim 8, wherein the processor is further configured toselect the plurality of paths based on a first user criteria receivedfrom a user.
 10. The system of claim 9, wherein the processor is furtherconfigured to filter the timing values for each of the plurality ofpaths based on a second user criteria received from the user.
 11. Thesystem of claim 8, wherein the parameterized model is a canonical slackmodel and the timing value is a slack.
 12. The system of claim 8,wherein the parameterized model is a canonical slew model and the timingvalue is a slew.
 13. The system of claim 8, wherein the parameterizedmodel is a canonical arrival time model and the timing value is anarrival time.
 14. The system of claim 8, wherein the set of timingperformance parameters comprises a temperature value, a voltage value,and a process value.
 15. A computer program product for statisticalstatic timing analysis of an integrated circuit, the computer programproduct comprising: a non-transitory storage medium readable by aprocessing circuit and storing instructions for execution by theprocessing circuit for performing a method comprising: performing aninitial statistical static timing analysis of the integrated circuit tocreate a parameterized model of the integrated circuit for a pluralityof paths using a plurality of timing corners to calculate a timing valuefor each of the plurality of paths, each of the plurality of timingcorners representing a set of timing performance parameters, determiningat least one worst timing corner from the parameterized model for eachof the plurality of paths based on the initial statistical static timinganalysis and calculated timing value for each of the plurality of paths,calculating a margin for each of the at least one worst timing corners;and performing a subsequent analysis of the integrated circuit using theat least one worst timing corner, wherein performing the subsequentanalysis further comprises applying the margin to the at least one worsttiming corner and performing the subsequent analysis using the margin,wherein the subsequent analysis is one of an optimization analysis and adeterministic timing analysis.